Overview

Dataset statistics

Number of variables14
Number of observations4362
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory477.2 KiB
Average record size in memory112.0 B

Variable types

Numeric14

Warnings

recency_p is highly correlated with avg_recency_daysHigh correlation
quantity_p is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_recency_days is highly correlated with recency_pHigh correlation
gross_revenue is highly correlated with quantity_p and 3 other fieldsHigh correlation
relative_revenue is highly correlated with quantity_p and 3 other fieldsHigh correlation
relative_quantity is highly correlated with quantity_p and 2 other fieldsHigh correlation
relative_invoices is highly correlated with gross_revenue and 1 other fieldsHigh correlation
recency_p is highly correlated with avg_recency_days and 1 other fieldsHigh correlation
quantity_p is highly correlated with avg_basket_size and 4 other fieldsHigh correlation
avg_ticket is highly correlated with avg_varietyHigh correlation
avg_recency_days is highly correlated with recency_pHigh correlation
avg_basket_size is highly correlated with quantity_p and 3 other fieldsHigh correlation
avg_variety is highly correlated with avg_ticketHigh correlation
gross_revenue is highly correlated with quantity_p and 4 other fieldsHigh correlation
relative_revenue is highly correlated with quantity_p and 4 other fieldsHigh correlation
relative_quantity is highly correlated with quantity_p and 4 other fieldsHigh correlation
relative_invoices is highly correlated with recency_p and 4 other fieldsHigh correlation
recency_p is highly correlated with avg_recency_daysHigh correlation
quantity_p is highly correlated with avg_basket_size and 3 other fieldsHigh correlation
avg_recency_days is highly correlated with recency_pHigh correlation
avg_basket_size is highly correlated with quantity_p and 1 other fieldsHigh correlation
gross_revenue is highly correlated with quantity_p and 3 other fieldsHigh correlation
relative_revenue is highly correlated with quantity_p and 3 other fieldsHigh correlation
relative_quantity is highly correlated with quantity_p and 3 other fieldsHigh correlation
relative_invoices is highly correlated with gross_revenue and 1 other fieldsHigh correlation
avg_recency_days is highly correlated with df_index and 1 other fieldsHigh correlation
quantity_d is highly correlated with quantity_p and 1 other fieldsHigh correlation
quantity_p is highly correlated with quantity_d and 5 other fieldsHigh correlation
df_index is highly correlated with avg_recency_days and 1 other fieldsHigh correlation
relative_invoices is highly correlated with quantity_p and 3 other fieldsHigh correlation
recency_p is highly correlated with avg_recency_days and 1 other fieldsHigh correlation
gross_revenue is highly correlated with quantity_p and 4 other fieldsHigh correlation
avg_ticket is highly correlated with avg_basket_sizeHigh correlation
avg_basket_size is highly correlated with quantity_p and 4 other fieldsHigh correlation
relative_revenue is highly correlated with quantity_p and 4 other fieldsHigh correlation
relative_quantity is highly correlated with quantity_d and 5 other fieldsHigh correlation
quantity_d is highly skewed (γ1 = 32.25098083) Skewed
gross_revenue is highly skewed (γ1 = 21.72254957) Skewed
relative_revenue is highly skewed (γ1 = 21.72254957) Skewed
relative_quantity is highly skewed (γ1 = 20.26712382) Skewed
df_index has unique values Unique
customer_id has unique values Unique
quantity_d has 2778 (63.7%) zeros Zeros
relative_invoices has 257 (5.9%) zeros Zeros

Reproduction

Analysis started2021-06-14 23:30:31.822739
Analysis finished2021-06-14 23:31:01.285519
Duration29.46 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct4362
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2837.824622
Minimum0
Maximum5970
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-14T20:31:01.386610image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile229.05
Q11303.25
median2733.5
Q34424.75
95-th percentile5635.95
Maximum5970
Range5970
Interquartile range (IQR)3121.5

Descriptive statistics

Standard deviation1758.838539
Coefficient of variation (CV)0.6197840859
Kurtosis-1.239013548
Mean2837.824622
Median Absolute Deviation (MAD)1551
Skewness0.1031975413
Sum12378591
Variance3093513.007
MonotonicityStrictly increasing
2021-06-14T20:31:01.546349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
53481
 
< 0.1%
33111
 
< 0.1%
53561
 
< 0.1%
33071
 
< 0.1%
12581
 
< 0.1%
33031
 
< 0.1%
12541
 
< 0.1%
12501
 
< 0.1%
14261
 
< 0.1%
Other values (4352)4352
99.8%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
59701
< 0.1%
59631
< 0.1%
59621
< 0.1%
59601
< 0.1%
59581
< 0.1%
59541
< 0.1%
59531
< 0.1%
59521
< 0.1%
59511
< 0.1%
59501
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct4362
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15301.81041
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-14T20:31:01.713415image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12615.05
Q113814.25
median15303.5
Q316780.75
95-th percentile17984.95
Maximum18287
Range5940
Interquartile range (IQR)2966.5

Descriptive statistics

Standard deviation1722.249578
Coefficient of variation (CV)0.1125520139
Kurtosis-1.196671423
Mean15301.81041
Median Absolute Deviation (MAD)1485
Skewness0.0005631275836
Sum66746497
Variance2966143.609
MonotonicityNot monotonic
2021-06-14T20:31:01.871194image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
163841
 
< 0.1%
136441
 
< 0.1%
136561
 
< 0.1%
177501
 
< 0.1%
157011
 
< 0.1%
136521
 
< 0.1%
177461
 
< 0.1%
177421
 
< 0.1%
177381
 
< 0.1%
138161
 
< 0.1%
Other values (4352)4352
99.8%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123491
< 0.1%
123501
< 0.1%
123521
< 0.1%
123531
< 0.1%
123541
< 0.1%
123551
< 0.1%
123561
< 0.1%
123571
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182811
< 0.1%
182801
< 0.1%
182781
< 0.1%
182771
< 0.1%
182761
< 0.1%
182741
< 0.1%
182731
< 0.1%

recency_p
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct304
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.99174691
Minimum0
Maximum373
Zeros34
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-14T20:31:02.038793image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q117
median51
Q3147
95-th percentile318
Maximum373
Range373
Interquartile range (IQR)130

Descriptive statistics

Standard deviation102.3910002
Coefficient of variation (CV)1.089361604
Kurtosis0.3847161405
Mean93.99174691
Median Absolute Deviation (MAD)41
Skewness1.237891749
Sum409992
Variance10483.91692
MonotonicityNot monotonic
2021-06-14T20:31:02.201861image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1103
 
2.4%
394
 
2.2%
494
 
2.2%
290
 
2.1%
879
 
1.8%
1077
 
1.8%
1774
 
1.7%
772
 
1.7%
971
 
1.6%
2264
 
1.5%
Other values (294)3544
81.2%
ValueCountFrequency (%)
034
 
0.8%
1103
2.4%
290
2.1%
394
2.2%
494
2.2%
548
1.1%
772
1.7%
879
1.8%
971
1.6%
1077
1.8%
ValueCountFrequency (%)
37317
 
0.4%
37218
0.4%
3716
 
0.1%
3693
 
0.1%
3685
 
0.1%
3675
 
0.1%
36610
 
0.2%
36543
1.0%
3646
 
0.1%
3626
 
0.1%

quantity_p
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct774
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean274.4497937
Minimum0
Maximum38639
Zeros33
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-14T20:31:02.373481image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18
Q160
median115
Q3233.75
95-th percentile702
Maximum38639
Range38639
Interquartile range (IQR)173.75

Descriptive statistics

Standard deviation1050.93695
Coefficient of variation (CV)3.829250283
Kurtosis519.6817487
Mean274.4497937
Median Absolute Deviation (MAD)69
Skewness19.0470736
Sum1197150
Variance1104468.473
MonotonicityNot monotonic
2021-06-14T20:31:02.543624image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2842
 
1.0%
6040
 
0.9%
6739
 
0.9%
5137
 
0.8%
7236
 
0.8%
5235
 
0.8%
6634
 
0.8%
033
 
0.8%
7033
 
0.8%
9032
 
0.7%
Other values (764)4001
91.7%
ValueCountFrequency (%)
033
0.8%
111
 
0.3%
25
 
0.1%
314
0.3%
46
 
0.1%
52
 
< 0.1%
614
0.3%
73
 
0.1%
85
 
0.1%
98
 
0.2%
ValueCountFrequency (%)
386391
< 0.1%
213521
< 0.1%
173761
< 0.1%
171501
< 0.1%
162881
< 0.1%
158531
< 0.1%
133691
< 0.1%
128721
< 0.1%
108281
< 0.1%
103991
< 0.1%

quantity_d
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct183
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.09903714
Minimum-0
Maximum9361
Zeros2778
Zeros (%)63.7%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-14T20:31:02.719378image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-0
5-th percentile-0
Q1-0
median-0
Q33
95-th percentile40.95
Maximum9361
Range9361
Interquartile range (IQR)3

Descriptive statistics

Standard deviation195.7888619
Coefficient of variation (CV)10.81763965
Kurtosis1332.556806
Mean18.09903714
Median Absolute Deviation (MAD)0
Skewness32.25098083
Sum78948
Variance38333.27843
MonotonicityNot monotonic
2021-06-14T20:31:02.875786image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-02778
63.7%
1349
 
8.0%
3173
 
4.0%
690
 
2.1%
289
 
2.0%
475
 
1.7%
545
 
1.0%
1245
 
1.0%
742
 
1.0%
840
 
0.9%
Other values (173)636
 
14.6%
ValueCountFrequency (%)
-02778
63.7%
1349
 
8.0%
289
 
2.0%
3173
 
4.0%
475
 
1.7%
545
 
1.0%
690
 
2.1%
742
 
1.0%
840
 
0.9%
937
 
0.8%
ValueCountFrequency (%)
93611
< 0.1%
48731
< 0.1%
40271
< 0.1%
23991
< 0.1%
23021
< 0.1%
21601
< 0.1%
16851
< 0.1%
16081
< 0.1%
15151
< 0.1%
13501
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct4300
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.31553376
Minimum0
Maximum3861
Zeros33
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-14T20:31:03.037134image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.355708812
Q111.98439402
median17.66815126
Q324.736875
95-th percentile90.50791667
Maximum3861
Range3861
Interquartile range (IQR)12.75248098

Descriptive statistics

Standard deviation110.1241737
Coefficient of variation (CV)3.305490301
Kurtosis544.0449692
Mean33.31553376
Median Absolute Deviation (MAD)6.432807882
Skewness19.83606126
Sum145322.3583
Variance12127.33363
MonotonicityNot monotonic
2021-06-14T20:31:03.206075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033
 
0.8%
154
 
0.1%
76.324
 
0.1%
1794
 
0.1%
18.73
 
0.1%
25.53
 
0.1%
24.42
 
< 0.1%
20.82
 
< 0.1%
302
 
< 0.1%
3582
 
< 0.1%
Other values (4290)4303
98.6%
ValueCountFrequency (%)
033
0.8%
2.1012857141
 
< 0.1%
2.1505882351
 
< 0.1%
2.2411
 
< 0.1%
2.2643751
 
< 0.1%
2.43251
 
< 0.1%
2.4623711341
 
< 0.1%
2.5048760331
 
< 0.1%
2.508371561
 
< 0.1%
2.547049181
 
< 0.1%
ValueCountFrequency (%)
38611
< 0.1%
30961
< 0.1%
2033.11
< 0.1%
2027.861
< 0.1%
1687.21
< 0.1%
1377.0777781
< 0.1%
1001.21
< 0.1%
952.98751
< 0.1%
931.51
< 0.1%
872.131
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1276
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.55606587
Minimum0
Maximum373
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-14T20:31:03.398528image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q134.58333333
median63.6
Q3122.6666667
95-th percentile298
Maximum373
Range373
Interquartile range (IQR)88.08333333

Descriptive statistics

Standard deviation86.50864489
Coefficient of variation (CV)0.9148925994
Kurtosis1.732227692
Mean94.55606587
Median Absolute Deviation (MAD)35.66666667
Skewness1.543552023
Sum412453.5593
Variance7483.745641
MonotonicityNot monotonic
2021-06-14T20:31:03.563046image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5329
 
0.7%
3024
 
0.6%
1823
 
0.5%
3922
 
0.5%
10622
 
0.5%
2421
 
0.5%
2520
 
0.5%
9220
 
0.5%
2820
 
0.5%
1519
 
0.4%
Other values (1266)4142
95.0%
ValueCountFrequency (%)
01
 
< 0.1%
14
0.1%
25
0.1%
2.5547945211
 
< 0.1%
39
0.2%
3.2434782611
 
< 0.1%
3.3008849561
 
< 0.1%
3.3333333331
 
< 0.1%
3.51
 
< 0.1%
3.6666666671
 
< 0.1%
ValueCountFrequency (%)
37315
0.3%
37217
0.4%
3717
0.2%
3693
 
0.1%
3685
 
0.1%
3675
 
0.1%
3668
0.2%
36510
0.2%
3645
 
0.1%
3626
 
0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2132
Distinct (%)48.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean224.1642092
Minimum0
Maximum7824
Zeros33
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-14T20:31:03.734675image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30.50833333
Q191
median160.3333333
Q3270
95-th percentile597.0020833
Maximum7824
Range7824
Interquartile range (IQR)179

Descriptive statistics

Standard deviation283.6856467
Coefficient of variation (CV)1.265526052
Kurtosis157.482962
Mean224.1642092
Median Absolute Deviation (MAD)81.66666667
Skewness8.854115445
Sum977804.2806
Variance80477.54614
MonotonicityNot monotonic
2021-06-14T20:31:03.902385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033
 
0.8%
10019
 
0.4%
8218
 
0.4%
8817
 
0.4%
12017
 
0.4%
7217
 
0.4%
13617
 
0.4%
7316
 
0.4%
7816
 
0.4%
10616
 
0.4%
Other values (2122)4176
95.7%
ValueCountFrequency (%)
033
0.8%
16
 
0.1%
1.51
 
< 0.1%
25
 
0.1%
32
 
< 0.1%
3.3333333331
 
< 0.1%
48
 
0.2%
53
 
0.1%
5.3333333331
 
< 0.1%
5.6666666671
 
< 0.1%
ValueCountFrequency (%)
78241
< 0.1%
43001
< 0.1%
42801
< 0.1%
3684.476191
< 0.1%
30281
< 0.1%
29241
< 0.1%
28801
< 0.1%
27081
< 0.1%
2697.4657531
< 0.1%
25291
< 0.1%

avg_variety
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1043
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.03462946
Minimum0
Maximum300.6470588
Zeros33
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-14T20:31:04.080906image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.333333333
Q19.2
median16.96774194
Q328
95-th percentile59
Maximum300.6470588
Range300.6470588
Interquartile range (IQR)18.8

Descriptive statistics

Standard deviation20.3113935
Coefficient of variation (CV)0.9217941941
Kurtosis18.75754511
Mean22.03462946
Median Absolute Deviation (MAD)8.878411911
Skewness3.032532776
Sum96115.05369
Variance412.552706
MonotonicityNot monotonic
2021-06-14T20:31:04.258122image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
197
 
2.2%
1390
 
2.1%
1484
 
1.9%
1082
 
1.9%
1181
 
1.9%
978
 
1.8%
675
 
1.7%
774
 
1.7%
571
 
1.6%
869
 
1.6%
Other values (1033)3561
81.6%
ValueCountFrequency (%)
033
 
0.8%
197
2.2%
1.21
 
< 0.1%
1.251
 
< 0.1%
1.3333333332
 
< 0.1%
1.58
 
0.2%
1.5555555561
 
< 0.1%
1.5714285711
 
< 0.1%
1.6666666674
 
0.1%
1.8333333331
 
< 0.1%
ValueCountFrequency (%)
300.64705881
< 0.1%
2191
< 0.1%
203.51
< 0.1%
1911
< 0.1%
1711
< 0.1%
1641
< 0.1%
1581
< 0.1%
1571
< 0.1%
1531
< 0.1%
1491
< 0.1%

purchases_pday
Real number (ℝ≥0)

Distinct1243
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4000047423
Minimum0
Maximum17
Zeros33
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2021-06-14T20:31:04.421487image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.009586200541
Q10.01989485241
median0.04474578328
Q31
95-th percentile1
Maximum17
Range17
Interquartile range (IQR)0.9801051476

Descriptive statistics

Standard deviation0.5593853893
Coefficient of variation (CV)1.398446894
Kurtosis177.5266664
Mean0.4000047423
Median Absolute Deviation (MAD)0.03322942607
Skewness6.699343025
Sum1744.820686
Variance0.3129120137
MonotonicityNot monotonic
2021-06-14T20:31:04.580197image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11501
34.4%
250
 
1.1%
033
 
0.8%
0.062518
 
0.4%
0.0277777777817
 
0.4%
0.0238095238117
 
0.4%
0.0909090909115
 
0.3%
0.0833333333314
 
0.3%
0.0294117647113
 
0.3%
0.0769230769213
 
0.3%
Other values (1233)2671
61.2%
ValueCountFrequency (%)
033
0.8%
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
 
< 0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
 
< 0.1%
0.005665722381
 
< 0.1%
0.0056818181822
 
< 0.1%
ValueCountFrequency (%)
171
 
< 0.1%
42
 
< 0.1%
33
 
0.1%
250
 
1.1%
1.1428571431
 
< 0.1%
11501
34.4%
0.751
 
< 0.1%
0.66666666674
 
0.1%
0.55882352941
 
< 0.1%
0.53887399461
 
< 0.1%

gross_revenue
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct4307
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1896.819324
Minimum-4287.63
Maximum279489.02
Zeros9
Zeros (%)0.2%
Negative42
Negative (%)1.0%
Memory size34.2 KiB
2021-06-14T20:31:04.746022image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-4287.63
5-th percentile101.343
Q1293.6175
median648.55
Q31612.625
95-th percentile5613.4125
Maximum279489.02
Range283776.65
Interquartile range (IQR)1319.0075

Descriptive statistics

Standard deviation8223.13024
Coefficient of variation (CV)4.335220617
Kurtosis607.4362874
Mean1896.819324
Median Absolute Deviation (MAD)455.145
Skewness21.72254957
Sum8273925.89
Variance67619870.94
MonotonicityNot monotonic
2021-06-14T20:31:04.899758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09
 
0.2%
76.324
 
0.1%
153
 
0.1%
35.43
 
0.1%
363.653
 
0.1%
4403
 
0.1%
318.052
 
< 0.1%
3312
 
< 0.1%
2042
 
< 0.1%
1202
 
< 0.1%
Other values (4297)4329
99.2%
ValueCountFrequency (%)
-4287.631
< 0.1%
-1592.491
< 0.1%
-1192.21
< 0.1%
-1165.31
< 0.1%
-11261
< 0.1%
-840.761
< 0.1%
-611.861
< 0.1%
-451.421
< 0.1%
-295.091
< 0.1%
-227.441
< 0.1%
ValueCountFrequency (%)
279489.021
< 0.1%
256438.491
< 0.1%
187482.171
< 0.1%
132572.621
< 0.1%
123725.451
< 0.1%
113384.141
< 0.1%
88125.381
< 0.1%
65892.081
< 0.1%
62653.11
< 0.1%
59419.341
< 0.1%

relative_revenue
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct4307
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1896.819324
Minimum-4287.63
Maximum279489.02
Zeros9
Zeros (%)0.2%
Negative42
Negative (%)1.0%
Memory size34.2 KiB
2021-06-14T20:31:05.057079image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-4287.63
5-th percentile101.343
Q1293.6175
median648.55
Q31612.625
95-th percentile5613.4125
Maximum279489.02
Range283776.65
Interquartile range (IQR)1319.0075

Descriptive statistics

Standard deviation8223.13024
Coefficient of variation (CV)4.335220617
Kurtosis607.4362874
Mean1896.819324
Median Absolute Deviation (MAD)455.145
Skewness21.72254957
Sum8273925.89
Variance67619870.94
MonotonicityNot monotonic
2021-06-14T20:31:05.209118image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09
 
0.2%
76.324
 
0.1%
153
 
0.1%
35.43
 
0.1%
363.653
 
0.1%
4403
 
0.1%
318.052
 
< 0.1%
3312
 
< 0.1%
2042
 
< 0.1%
1202
 
< 0.1%
Other values (4297)4329
99.2%
ValueCountFrequency (%)
-4287.631
< 0.1%
-1592.491
< 0.1%
-1192.21
< 0.1%
-1165.31
< 0.1%
-11261
< 0.1%
-840.761
< 0.1%
-611.861
< 0.1%
-451.421
< 0.1%
-295.091
< 0.1%
-227.441
< 0.1%
ValueCountFrequency (%)
279489.021
< 0.1%
256438.491
< 0.1%
187482.171
< 0.1%
132572.621
< 0.1%
123725.451
< 0.1%
113384.141
< 0.1%
88125.381
< 0.1%
65892.081
< 0.1%
62653.11
< 0.1%
59419.341
< 0.1%

relative_quantity
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct785
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean256.3507565
Minimum-2887
Maximum37684
Zeros15
Zeros (%)0.3%
Negative47
Negative (%)1.1%
Memory size34.2 KiB
2021-06-14T20:31:05.369993image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-2887
5-th percentile16
Q157
median110
Q3219
95-th percentile673.9
Maximum37684
Range40571
Interquartile range (IQR)162

Descriptive statistics

Standard deviation981.5809106
Coefficient of variation (CV)3.829054081
Kurtosis595.7323081
Mean256.3507565
Median Absolute Deviation (MAD)67
Skewness20.26712382
Sum1118202
Variance963501.084
MonotonicityNot monotonic
2021-06-14T20:31:05.533914image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2839
 
0.9%
6737
 
0.8%
5136
 
0.8%
7234
 
0.8%
6034
 
0.8%
4832
 
0.7%
4632
 
0.7%
6931
 
0.7%
8431
 
0.7%
7531
 
0.7%
Other values (775)4025
92.3%
ValueCountFrequency (%)
-28871
< 0.1%
-1891
< 0.1%
-1571
< 0.1%
-1511
< 0.1%
-1441
< 0.1%
-981
< 0.1%
-711
< 0.1%
-701
< 0.1%
-691
< 0.1%
-562
< 0.1%
ValueCountFrequency (%)
376841
< 0.1%
213521
< 0.1%
152251
< 0.1%
149901
< 0.1%
149771
< 0.1%
128711
< 0.1%
128691
< 0.1%
122611
< 0.1%
102291
< 0.1%
95631
< 0.1%

relative_invoices
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct56
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.407611188
Minimum-6
Maximum195
Zeros257
Zeros (%)5.9%
Negative82
Negative (%)1.9%
Memory size34.2 KiB
2021-06-14T20:31:05.702553image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-6
5-th percentile0
Q11
median2
Q34
95-th percentile11
Maximum195
Range201
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.326680805
Coefficient of variation (CV)1.856632244
Kurtosis292.1130986
Mean3.407611188
Median Absolute Deviation (MAD)1
Skewness12.77530276
Sum14864
Variance40.02689
MonotonicityNot monotonic
2021-06-14T20:31:05.863186image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11520
34.8%
2804
18.4%
3480
 
11.0%
4347
 
8.0%
0257
 
5.9%
5215
 
4.9%
6163
 
3.7%
795
 
2.2%
-172
 
1.7%
865
 
1.5%
Other values (46)344
 
7.9%
ValueCountFrequency (%)
-61
 
< 0.1%
-31
 
< 0.1%
-28
 
0.2%
-172
 
1.7%
0257
 
5.9%
11520
34.8%
2804
18.4%
3480
 
11.0%
4347
 
8.0%
5215
 
4.9%
ValueCountFrequency (%)
1951
< 0.1%
1541
< 0.1%
831
< 0.1%
791
< 0.1%
761
< 0.1%
701
< 0.1%
641
< 0.1%
582
< 0.1%
511
< 0.1%
501
< 0.1%

Interactions

2021-06-14T20:30:34.101228image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:34.244228image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:34.383110image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:34.516611image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:34.651403image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:34.789058image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:34.943073image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:35.085588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:35.232078image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:35.371884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:35.514692image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:35.661503image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:35.809274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:35.954246image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:36.088709image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:36.219783image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:36.343309image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:36.484273image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:36.627446image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:36.767071image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:36.899583image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:37.033291image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:37.171003image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:37.299367image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:37.430804image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:37.566567image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:37.701015image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:37.833706image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:37.960092image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:38.088296image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:38.229003image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:38.375292image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:38.532726image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:38.665190image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:38.806656image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:38.938420image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:39.068840image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:39.201686image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:39.356237image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:39.495404image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:39.620466image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:39.761803image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:39.909387image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:40.054066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:40.187928image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:40.326612image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:40.472813image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:40.611818image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:40.749079image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:40.891340image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:41.044244image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:41.190534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:41.341679image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:41.486904image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:41.625109image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:41.767291image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:41.906636image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:42.037110image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:42.168857image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:42.301970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:42.441325image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:42.568502image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:42.704787image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:42.839255image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:42.993876image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:43.124074image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:43.252658image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:43.375066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:43.498373image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:43.635044image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:43.802867image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:43.948147image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:44.077632image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:44.211793image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:44.355583image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:44.483959image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:44.620977image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:44.752096image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:44.887586image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:45.023061image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:45.167633image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:45.295039image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:45.421864image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:45.550852image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:45.673479image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:45.799257image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:45.922492image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:46.050165image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:46.182253image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:46.303242image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:46.432366image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:46.559231image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:46.689759image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:46.820700image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:46.952302image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:47.105057image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:47.234415image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:47.369039image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:47.503680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:47.634964image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:47.763321image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:47.893794image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:48.029371image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:48.156261image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:48.294986image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:48.429766image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:48.564162image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:48.696627image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:48.833272image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:48.964458image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:49.094231image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:49.234075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:49.369539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:49.494156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:49.618503image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:49.743844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:49.874460image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:49.995602image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:50.123301image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:50.251667image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:50.384812image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:50.509534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:50.635417image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:50.758818image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:50.879711image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:51.006658image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:51.135359image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:51.273130image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:51.410682image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:51.556613image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:51.697137image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:51.840894image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:51.990411image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:52.127670image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:52.269215image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:52.406102image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:52.547217image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:52.683851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:52.821148image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:52.966032image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:53.112685image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:53.246718image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:53.376225image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:53.509114image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:53.643348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:53.770060image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:53.909258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:54.042181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:54.178307image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:54.311204image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:54.440810image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:54.566240image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:54.700751image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:54.837613image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:54.976435image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:55.116488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:55.251683image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:55.385509image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:55.522813image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:55.652686image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:55.784531image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:55.924070image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:56.056424image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:56.182907image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:56.312951image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:56.440504image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:56.565191image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:56.706631image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:56.843440image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:56.981980image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:57.118750image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:57.259749image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:57.402345image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:57.537517image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:57.678239image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:57.815935image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:57.957980image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:58.090317image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:58.226418image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:58.354650image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:58.489280image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:58.627035image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:58.763692image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:58.902061image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:59.039830image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:59.181449image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:59.322918image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:59.455914image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:59.602453image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:59.741316image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:30:59.877479image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:31:00.006499image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:31:00.144441image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:31:00.277905image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:31:00.407603image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-14T20:31:00.557128image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-06-14T20:31:06.015817image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-14T20:31:06.231081image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-14T20:31:06.439258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-14T20:31:06.649374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-06-14T20:31:00.842473image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-14T20:31:01.182608image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idrecency_pquantity_pquantity_davg_ticketavg_recency_daysavg_basket_sizeavg_varietypurchases_pdaygross_revenuerelative_revenuerelative_quantityrelative_invoices
0017850372.035.021.018.152222124.33333350.9705888.73529417.0000005288.635288.6314.033.0
111304731.0132.06.018.82290726.642857139.10000017.2000000.0291553079.103079.10126.02.0
22125832.01569.050.029.47927120.722222337.33333316.4666670.0403237187.347187.341519.012.0
331374895.0169.0-0.033.86607193.25000087.8000005.6000000.017921948.25948.25169.05.0
4415100333.048.022.0292.00000062.16666726.6666671.0000000.073171635.10635.1026.00.0
551529125.0508.027.045.32330121.941176140.2000006.8666670.0429804596.514596.51481.010.0
66146887.0579.0281.017.21978617.761905172.42857115.5714290.0572215107.385107.38298.015.0
771780916.0961.041.088.71983631.083333171.4166675.0833330.0335204627.624627.62920.09.0
88153110.02167.0231.025.5434644.098901419.71428626.1428570.24331659419.3459419.341936.064.0
99145272.0198.03.08.7539305.82812537.98181817.6727270.1494577711.387711.38195.024.0

Last rows

df_indexcustomer_idrecency_pquantity_pquantity_davg_ticketavg_recency_daysavg_basket_sizeavg_varietypurchases_pdaygross_revenuerelative_revenuerelative_quantityrelative_invoices
43525950160002.0770.0-0.01377.0777782.01703.3333333.03.012393.7012393.70770.03.0
43535951151952.01404.0-0.03861.0000002.01404.0000001.01.03861.003861.001404.01.0
43545952140872.0113.01.02.8176812.0251.00000069.01.0181.67181.67112.00.0
43555953142042.021.0-0.03.6597732.082.00000044.01.0161.03161.0321.01.0
43565954154712.0102.0-0.06.0971432.0266.00000077.01.0469.48469.48102.01.0
43575958134361.058.0-0.016.4075001.076.00000012.01.0196.89196.8958.01.0
43585960155201.0134.0-0.019.0833331.0314.00000018.01.0343.50343.50134.01.0
43595962132981.096.0-0.0180.0000001.096.0000002.01.0360.00360.0096.01.0
43605963145691.070.0-0.018.9491671.079.00000012.01.0227.39227.3970.01.0
43615970127130.0101.0-0.022.3302630.0508.00000038.01.0848.55848.55101.01.0